SQL & data pipelines
Find expensive joins, unnecessary shuffles, repeated work, and data layout issues in Spark SQL and DataFrame workloads.
Query plans · Joins · File layout
Performance tuning.For your workloads.
Work directly with the team behind Sail to find bottlenecks and improve performance across your workloads.
Hands-on help with queries, data layout, Python processing, and compute configuration for teams using open-source Sail or LakeSail Platform.
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Tell us about your team and workloads.
- 01 Your team
- 02 Workloads
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WHERE WE CAN HELP
Queries, pipelines, and configuration.
Performance depends on how your code, data, and infrastructure work together. We investigate the parts that matter for your workloads.
Compute & configuration
Tune Sail settings, parallelism, batch sizes, and worker resources around your workloads. Investigate memory pressure and uneven resource use.
Engine settings · Parallelism · Worker sizing
Python & AI workloads
Review UDF execution, model initialization, and batch processing. Reduce repeated work in preprocessing, embedding, and scoring pipelines.
Python UDFs · Model reuse · Throughput
Moving workloads to Sail
Evaluate representative Spark workloads on Sail, check compatibility and output correctness, and identify changes before a wider rollout.
Compatibility · Correctness · Evaluation